This study examines LLM-based AI chatbots as social interaction partners. It identifies young adults’ support-seeking motivations and models their associations with two interaction processes (Perceived Authenticity and Interaction Intensity) and two psychological outcomes (Resilience and AI Dependence). In Study 1, we developed the Motivation Scale for Generative AI (MS-GAI) through theory-driven item generation and interviews and validated it with a survey of Korean young adults (N = 300). Exploratory and confirmatory factor analyses supported an eight-factor, 40-item structure grounded in four social-support domains and supplemented by context-specific motivations. In Study 2, structural equation modeling showed that Emotional Support and Self-Enhancement were positively associated with both Perceived Authenticity and Interaction Intensity. Objective Advice Seeking was positively associated with Perceived Authenticity, whereas Learning Assistance, Daily Problem-Solving, and Entertainment/Mood Diversion were positively associated with Interaction Intensity. Perceived Authenticity was also positively associated with Interaction Intensity. Both interaction processes were positively related to self-reported Resilience and AI Dependence, with Interaction Intensity showing the stronger association with AI Dependence. These findings identify distinct but overlapping interaction pathways through which support-seeking motivations are associated with adaptive and maladaptive outcomes. The study extends understanding of social support in human–AI interaction by providing a validated motivation measure and showing how shared interaction processes relate to both short-term perceived recovery and dependence. The findings are consistent with the Paradoxes of Technology perspective and highlight the need for emotionally supportive yet balanced design and policy safeguards.
Motivated by the demographic effects, we propose and analyze a novel opinion dynamics model with density-dependent interactions and a nonlinear communication function reminiscent of the celebrated Hegselmann–Krause (H–K) model. The model also incorporates heterogeneity through intrinsic opinion tendencies, leading to richer dynamics. In the homogeneous case, where all agents share the same intrinsic tendency, the relative dissimilarities between opinions converge to zero and a single cluster forms. In contrast, under heterogeneous tendencies, we prove that multiple clusters emerge together with velocity alignment within each cluster, provided suitable structural conditions on the system parameters. These theoretical findings are further supported by numerical simulations, which offer qualitative insights beyond the analytical results.
High-voltage rotating-machines frequently fail due to insulation degradation; however, accelerated life-test (ALT) data are scarce and heavily censored. Moreover, conventional ALT models analyze each voltage class independently, which limits cross-class extrapolation. To address these challenges, this study develops RIPPLE (Rated-voltage-Informed Pseudo-Probabilistic Lifetime Estimation), a Bayesian accelerated failure-time framework that integrates experimental data and specification information to improve lifetime inference. First, censored specimens are transformed into pseudo-labeled lifetimes by learning a mapping from diagnostic time series and design features to failure time using a lightweight Transformer-MLP model ensemble, with predictive uncertainty explicitly quantified. Second, rated-voltage R is incorporated as a design covariate in a LogNormal rated-voltage-informed Temperature-Nonthermal (R-TNT) stress-life model, enabling joint modeling across heterogeneous voltage classes. Third, pseudo-labels are treated as uncertain soft evidence through a truncated latent-variable likelihood, propagating prediction uncertainty into the posterior and enforcing censoring consistency probabilistically. Applied to multi-stress ALT data on 6.6/13.8 kV form-wound coils, RIPPLE reduced the 90% credible-interval width by approximately 90% at 155 degrees C and 2.1 R relative to the censored-likelihood baseline, while substantially improving point-prediction accuracy (MAPE 0.453 vs. 2.213). Simulation studies further support the effectiveness of the proposed framework.
We correct an error in the proof of the main result of our original paper.
The synergistic coupling of dry reforming of methane and the reverse water-gas shift reaction (SCDR) offers an effective route for CO2 utilization under CO2-rich conditions. In this study, Ni-MgO-CeO2-Al2O3 (NMCA)catalysts were designed to elucidate the distinct roles of surface and interfacial oxygen vacancies. The catalytic activity is shown to be primarily governed by the density of interfacial oxygen vacancies at Ni-O-Ce interfaces rather than the total oxygen storage capacity of catalyst. Moderate Ce incorporation results in highly dispersed CeO2 species in close proximity to Ni, promoting the formation of interfacial defect sites that anchor and stabilize small Ni particles while maximizing the number of accessible active sites. In contrast, excessive Ce leads to the formation of bulk CeO2 domains, which increase oxygen storage capacity but reduce the density of interfacial defect sites, resulting in decreased catalytic performance. In-situ DRIFTS analysis reveals that catalysts enriched with Ni-O-Ce interfacial sites enable rapid turnover of CO2-derived intermediates without significant surface accumulation, facilitating efficient CO formation. These findings highlight that interfacial defect structures play a critical role in controlling CO2 activation and reaction pathways under CO2-rich SCDR conditions, providing a rational basis for designing efficient catalysts for CO2 conversion.
